In many dynamic systems, such as database management systems, operationally significant behavior occurs at fine time granularity. Optimizing system operational performance and efficiency requires understanding fine-grain behavior over long time durations. This understanding provides visibility into the impacts, patterns, and causes of behavior affecting operational performance. Examples include frequency, duration, and temporal patterns of occurrences of operationally significant fine-grain behavior.
There are many existing methods for understanding dynamic behavior represented by time series. However, when applied over long time durations these methods do not provide visibility into operational behavior: (1) methods are based on coarse-grain numerical statistical summarization over long time durations and mask fine-grain behavior; or (2) methods based on fine-grain numerical techniques do not directly discover operationally meaningful behavior and miss important random, short duration events.